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Statistical modeling of sensor technology data

Ireland

Analysis of x-ray images with mathematical modeling

Chile - Ireland - Venezuela

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Inference in stochastic differential equations using Monte Carlos sequential methods

Evaluation of the progress of the caused pandemic by SARSCOV-2 in the Portoviejo canton of the Manabí province, through a stratified sampling by parishes

Temporary space model for prediction of daily rainfall

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Colombia

Ecuador

Venezuela

Venezuela

Using sensor data from petroleum industry

Using sensor data from manufacturing processes

Using data from health systems

Using data from health systems

Inference in stochastic differential equations using Monte Carlos sequential methods

Using sensor data from earthquakes

Using sensor data from meteorological sensors

Reconstruction of chaotic dynamic systems, using non-linear filters

Ecuador

Data from are now routinely measured in X-ray images. The development of interpretable, computationally efficient models suitable for x-ray images is an important enabling technology for answering health-monitoring-relevant questions. This project will develop novel statistical tools and algorithms for de-noising x-ray images. ​

Data from are now routinely measured in manufacturing, however the data are rarely utilized to their full potential, if at all. The development of interpretable, computationally efficient models suitable for high-throughput data is an important enabling technology for answering industry-relevant questions. This project developed novel statistical tools and algorithms for the modelling of multivariate sensor data. ​

This project proposed a based methodology on recursive filtering algorithms that allows the estimation of stochastic differential equations. The methodology was applied that is using stochastic models that specifically come from the world of oil exploration.

Evaluate the epidemiological behavior of COVID-19 that was using the SEIR models with the obtained data from a two-stage sampling in the Portoviejo canton of the Manabí Province. It allowed to evaluates the behavior of the epidemiological variables of COVID-19, predict the dynamics of the pandemic with 95% reliability, and establish epidemiological fences to focus actions in the territory.

This project proposed a based methodology on recursive filtering algorithms that allows the estimation of stochastic differential equations. The massive increases in data volumes with these characteristics that are generated by radars, satellites and meteorological stations, that is required to use high performance computational tools for analysis, estimation and prediction with dynamic stochastic models. The methodology was applied that is using stochastic models that specifically come from the world of rainfall phenomenon.

This project proposed a based methodology on recursive filtering algorithms that allows the estimation of stochastic differential equations. The methodology was applied that is using stochastic models that specifically come from the world of seismic events.

This project proposed a based methodology on recursive filtering algorithms that allows the estimation of stochastic models. The methodology was applied that is using chaotic models that specifically come from the basic structure of synthetic electrocardiogram signals.

They trusted our expertise to unlock the full potential of their sensor data and gained a competitive edge in their industry